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"abstract": "CT colonography (CTC) is a rapidly evolving technique to screen for colorectal polyps. Fecal residue may occlude or, reversely, mimic polyps. Electronic cleansing aims at removing contrast-enhanced fecal residue from the image. However, thin layers of soft tissue (the colon wall or a fold) or residue are easily misclassified by current electronic cleansing methods, thereby causing holes in the colon wall or other artefacts that hamper visualization and automated detection. We present a thin layer model to detect and characterize such layers to support electronic cleansing. It is demonstrated that the model sustains robust estimation of the location and thickness of such a layer. Such thicknesses of thin layers were measured in real data sets. A lower bound on the thickness of such layers exists and was found to be 1.0 mm for our data.",
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"abstract": "Colorectal cancer is a leading cause of cancer deaths, estimated 696 thousand worldwide. Recent years have seen an increase of deep learning techniques and algorithms being used to detect colon polyps. In this work we address colon polyp detection using Convolutional Neural Networks (CNNs) combined with Autoencoders. We use 3 publicly available databases namely: CVC-ColonDB, CVC-ClinicDB and ETIS-LaribPolypDB, to train the model. The results obtained in terms of accuracy are: 0.937, 0.951, 0.967 for the above-mentioned databases respectively. Due to the nature of the colon polyps, diverse shapes and characteristics, there is still place for improvements.",
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"title": "Vascular Morphogenesis Controller: A Distributed Controller for Growing Artificial Structures",
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"abstract": "It is a challenging task to develop morphologies of structures in response to dynamic environmental factors and constraints. In the context of the EU-funded project flora robotica [1] we are interested in developing selforganized methods that combine local considerations and global requirements and drive the development of structures. Embryogenetic development of biological organisms and cell differentiation are studied for a long time in evolutionary developmental biology (EvoDevo) [2], [3]. Some of the mechanisms from that field are already applied to pattern formation [4] and development of body morphologies [5], [6] and controllers [7] in evolutionary robotics [8] and modular robotics [9]. In this work, vascular system and branching dynamics of plants are used as the source of inspiration for designing a novel algorithm called \"Vascular Morphogenesis Controller\" (VMC) that is applied to morphological development of modular structures. Plant vessels develop in the stems and roots. They transport water and minerals from the roots to the leaves, and sugars and photosynthates from the leaves to other parts of the plant [10]. There are evidences [11], [12] suggesting that there is a competition between different branches over the vascular growth. The branches that are in better situations (e.g., get more light) produce more photosynthates that flow back from the leaves. The higher flow rate leads to more vascular tissues in the branch and therefore more water and minerals from the roots reach the branch. More water and minerals facilitate the growth of the branch and the branch may end up in an even better situation which in turn reinforces the growth. Different branches with their different local conditions compete over production of new vessels. On the other hand, global resources (i.e., water) are limited and the vessels are subject to degradation as well. Based on the positive and negative feedback loops established by this competition and limitation, a dynamic system of vessels shape the growth of the plants.",
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"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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"title": "Detecting Chronic Vascular Damage with Attention-Guided Neural System",
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"abstract": "The retinal vasculature has a vital role in predicting chronic diabetic and hypertensive retinopathy. Recently, the advent of deep learning algorithms has brought a revolution in ocular disease prediction. The researchers frequently design complex and intricate techniques to efficiently segment vessels, micro-vessels and achieve better response on publicly available benchmark datasets. This article has designed an attention-guided neural system to extract vascular tree and distinguish it in arteries and veins. The proposed learning protocol with a minimalist approach can compete with state-of-the-art work without a performance compromise. Our method has achieved a promising response on numerous retinal image datasets. The pitfall of previously proposed work is also addressed through the self-defined assessment criteria. The in-depth analysis highlights that the underlying problem is unsolved for unseen data with different distribution than training. Our method is cross-validated to report the performance loss by keeping diversity in data selection. The technique is further applied for the arteries and veins extraction. Our effort can be adapted as an efficient vision-critical platform to scan and localize retinal damage and diagnose the disease symptoms early to prevent vision impairment.",
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"content": "The retinal vasculature has a vital role in predicting chronic diabetic and hypertensive retinopathy. Recently, the advent of deep learning algorithms has brought a revolution in ocular disease prediction. The researchers frequently design complex and intricate techniques to efficiently segment vessels, micro-vessels and achieve better response on publicly available benchmark datasets. This article has designed an attention-guided neural system to extract vascular tree and distinguish it in arteries and veins. The proposed learning protocol with a minimalist approach can compete with state-of-the-art work without a performance compromise. Our method has achieved a promising response on numerous retinal image datasets. The pitfall of previously proposed work is also addressed through the self-defined assessment criteria. The in-depth analysis highlights that the underlying problem is unsolved for unseen data with different distribution than training. Our method is cross-validated to report the performance loss by keeping diversity in data selection. The technique is further applied for the arteries and veins extraction. Our effort can be adapted as an efficient vision-critical platform to scan and localize retinal damage and diagnose the disease symptoms early to prevent vision impairment.",
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"authors": [
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"affiliation": "Kansas State University,Department of Computer Science,Manhattan,USA",
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"affiliation": "Quaid-i-Azam University,Department of Computer Science,Islamabad,Pakistan",
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"title": "Design of Vascular Interventional Surgical Robot with Network Time Delay Analysis for Master-slave Teleoperation",
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"abstract": "Intravascular catheterization is a vital task performed by surgeons during interventional treatment of vascular diseases. The procedure is reliant on achieving a precise and safe manipulation of endovascular tools from the entry port to the target location within the patient vasculature. However, during such procedures inefficient proximal-to-distal transmissions could occur due to imprecise motion control, communication delay as well as the application of an excessive force. Thus achieving a precise control of endovascular tools during procedures remain an active research area. This study proposes a master-slave interventional (MSI) robot prototype for precise control of guide wire axial and rotational motion within the vasculature. The mechanism of the MSI robot mimics the natural operating process of a surgeon and therefore requires little learning cost. And the system’s force feedback module improves the robot’s transparency. Furthermore, a multi-connection time-delay control approach is proposed to minimize communication delay and improve the stability of the MSI robot. The developed prototype was utilized for in-vivo experiments with the experimental outcome demonstrating its versatility for vascular intervention.",
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"content": "Intravascular catheterization is a vital task performed by surgeons during interventional treatment of vascular diseases. The procedure is reliant on achieving a precise and safe manipulation of endovascular tools from the entry port to the target location within the patient vasculature. However, during such procedures inefficient proximal-to-distal transmissions could occur due to imprecise motion control, communication delay as well as the application of an excessive force. Thus achieving a precise control of endovascular tools during procedures remain an active research area. This study proposes a master-slave interventional (MSI) robot prototype for precise control of guide wire axial and rotational motion within the vasculature. The mechanism of the MSI robot mimics the natural operating process of a surgeon and therefore requires little learning cost. And the system’s force feedback module improves the robot’s transparency. Furthermore, a multi-connection time-delay control approach is proposed to minimize communication delay and improve the stability of the MSI robot. The developed prototype was utilized for in-vivo experiments with the experimental outcome demonstrating its versatility for vascular intervention.",
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"Catheters",
"Control Engineering Computing",
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"Medical Computing",
"Medical Image Processing",
"Medical Robotics",
"Motion Control",
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"Surgery",
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"Network Time Delay Analysis",
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"Intravascular Catheterization",
"Vital Task",
"Interventional Treatment",
"Vascular Diseases",
"Precise Manipulation",
"Safe Manipulation",
"Endovascular Tools",
"Entry Port",
"Target Location",
"Patient Vasculature",
"Proximal To Distal Transmissions",
"Motion Control",
"Communication Delay",
"Excessive Force",
"Master Slave Interventional Robot Prototype",
"Guide Wire Axial",
"Rotational Motion",
"MSI Robot Mimics",
"Natural Operating Process",
"Surgeon",
"System",
"Multiconnection Time Delay Control Approach",
"Vascular Intervention",
"Wires",
"Force",
"Prototypes",
"Surgery",
"Master Slave",
"Tools",
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"Vascular Interventional Robot",
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"affiliation": "Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China,100876",
"fullName": "Yifa Li",
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"affiliation": "Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China,100876",
"fullName": "Weimin Wang",
"givenName": "Weimin",
"surname": "Wang",
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"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,Research Center of Medical Robot and Minimally Invasive Surgical Devices,Shenzhen,China,518055",
"fullName": "Wenke Duan",
"givenName": "Wenke",
"surname": "Duan",
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{
"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,Research Center of Medical Robot and Minimally Invasive Surgical Devices,Shenzhen,China,518055",
"fullName": "Omisore Olatunji Mumini",
"givenName": "Omisore",
"surname": "Olatunji Mumini",
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"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,Research Center of Medical Robot and Minimally Invasive Surgical Devices,Shenzhen,China,518055",
"fullName": "Toluwanimi Akinyemi",
"givenName": "Toluwanimi",
"surname": "Akinyemi",
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{
"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,Research Center of Medical Robot and Minimally Invasive Surgical Devices,Shenzhen,China,518055",
"fullName": "Wenjing Du",
"givenName": "Wenjing",
"surname": "Du",
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"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,Research Center of Medical Robot and Minimally Invasive Surgical Devices,Shenzhen,China,518055",
"fullName": "Yuhong Zheng",
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"abstract": "We have developed a useful system to automatically segment colonic wall based on anatomical knowledge form a series of abdomen computer tomography volume dataset. It employs distance from boundary, potential field and volumetric rendering to identify the true polyps inside human colon. The proposed system also provides an interactive user interface for navigation with virtual rendering and is applied to the sub-volume of polyp candidates with 3-D shape, gray level and curvature. It can efficiently detect polyps with two-dimensional viewing images parallel to axial, sagittal and coronal in an interactive manner. With reconstruction of CT volume data, a virtual colonography system can be used to simulate the examination of fiber optical colonography. It can help the radiologists classify the polyps in clinical diagnosis.",
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"content": "We have developed a useful system to automatically segment colonic wall based on anatomical knowledge form a series of abdomen computer tomography volume dataset. It employs distance from boundary, potential field and volumetric rendering to identify the true polyps inside human colon. The proposed system also provides an interactive user interface for navigation with virtual rendering and is applied to the sub-volume of polyp candidates with 3-D shape, gray level and curvature. It can efficiently detect polyps with two-dimensional viewing images parallel to axial, sagittal and coronal in an interactive manner. With reconstruction of CT volume data, a virtual colonography system can be used to simulate the examination of fiber optical colonography. It can help the radiologists classify the polyps in clinical diagnosis.",
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"normalizedAbstract": "We have developed a useful system to automatically segment colonic wall based on anatomical knowledge form a series of abdomen computer tomography volume dataset. It employs distance from boundary, potential field and volumetric rendering to identify the true polyps inside human colon. The proposed system also provides an interactive user interface for navigation with virtual rendering and is applied to the sub-volume of polyp candidates with 3-D shape, gray level and curvature. It can efficiently detect polyps with two-dimensional viewing images parallel to axial, sagittal and coronal in an interactive manner. With reconstruction of CT volume data, a virtual colonography system can be used to simulate the examination of fiber optical colonography. It can help the radiologists classify the polyps in clinical diagnosis.",
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"Optical Colonoscopy",
"Distance Form Boundary",
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{
"affiliation": "National Chia-Yi University, R.O.C.",
"fullName": "Chien-Chuan Ko",
"givenName": "Chien-Chuan",
"surname": "Ko",
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"affiliation": "National Chia-Yi University, R.O.C.",
"fullName": "Jyh-Wei Jang",
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"title": "2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT)",
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"doi": "10.1109/ICRAECT.2017.29",
"title": "Principal Curvature Based Polyp Detection in Wireless Capsule Endoscopy Images",
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"abstract": "Polyp identification in human Gastro Intestinal (GI) Tract employing endoscopy and colonoscopy through various computer vision and image processing techniques has achieved lot of success and maturity in its field. Over the last decade Wireless Capsule Endoscopy (WCE) has emerged as an innovative diagnostic system for identification of ailments such as polyps in GI tract. Polyp detection through WCE suffers from various types of challenges due to its unique image acquisition technique. In the proposed work, Principal curvature based polyp detection technique inWCE is presented. The experimental results were conducted on 58 WCE images comprising of various types of polyps. The results have revealed that an adaptive geometrical feature extraction based identification method of polyps is suggested for efficient analysis. Further, the challenges associated with detection of polyps are also discussed.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Polyp identification in human Gastro Intestinal (GI) Tract employing endoscopy and colonoscopy through various computer vision and image processing techniques has achieved lot of success and maturity in its field. Over the last decade Wireless Capsule Endoscopy (WCE) has emerged as an innovative diagnostic system for identification of ailments such as polyps in GI tract. Polyp detection through WCE suffers from various types of challenges due to its unique image acquisition technique. In the proposed work, Principal curvature based polyp detection technique inWCE is presented. The experimental results were conducted on 58 WCE images comprising of various types of polyps. The results have revealed that an adaptive geometrical feature extraction based identification method of polyps is suggested for efficient analysis. Further, the challenges associated with detection of polyps are also discussed.",
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"normalizedAbstract": "Polyp identification in human Gastro Intestinal (GI) Tract employing endoscopy and colonoscopy through various computer vision and image processing techniques has achieved lot of success and maturity in its field. Over the last decade Wireless Capsule Endoscopy (WCE) has emerged as an innovative diagnostic system for identification of ailments such as polyps in GI tract. Polyp detection through WCE suffers from various types of challenges due to its unique image acquisition technique. In the proposed work, Principal curvature based polyp detection technique inWCE is presented. The experimental results were conducted on 58 WCE images comprising of various types of polyps. The results have revealed that an adaptive geometrical feature extraction based identification method of polyps is suggested for efficient analysis. Further, the challenges associated with detection of polyps are also discussed.",
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"Silicon",
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"Geometrical Feature Extraction",
"Shape Index",
"Curvedness"
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"fullName": "K. V. Mahendra Prashanth",
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"abstract": "In this paper, we report an evaluation study of predictive models of programmer navigation. In particular, we compared two operationalizations of navigation from the literature (click-based versus view-based) to see which more accurately records a developer's navigation behaviors. Moreover, we also compared the predictive accuracy of seven models of programmer navigation from the literature, including ones based on navigation history and code-structural relationships. To address our research goals, we performed a controlled laboratory study of the navigation behavior of 10 participants engaged in software evolution tasks. The study was a partial replication of a previous comprehensive evaluation of predictive models by Piorkowski et al., and also served to test the generalizability of their results. Key findings of the study included that the click-based navigations agreed closely with those reported by human observers, whereas view-based navigations diverged significantly. Furthermore, our data showed that the predictive model based on recency was significantly more accurate than the other models, suggesting the strong potential for tools that leverage recency-type models. Finally, our model-accuracy results had a strong correlation with the Piorkowski results, however, our results differed in several noteworthy ways, potentially caused by differences in task type and code familiarity.",
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"content": "In this paper, we report an evaluation study of predictive models of programmer navigation. In particular, we compared two operationalizations of navigation from the literature (click-based versus view-based) to see which more accurately records a developer's navigation behaviors. Moreover, we also compared the predictive accuracy of seven models of programmer navigation from the literature, including ones based on navigation history and code-structural relationships. To address our research goals, we performed a controlled laboratory study of the navigation behavior of 10 participants engaged in software evolution tasks. The study was a partial replication of a previous comprehensive evaluation of predictive models by Piorkowski et al., and also served to test the generalizability of their results. Key findings of the study included that the click-based navigations agreed closely with those reported by human observers, whereas view-based navigations diverged significantly. Furthermore, our data showed that the predictive model based on recency was significantly more accurate than the other models, suggesting the strong potential for tools that leverage recency-type models. Finally, our model-accuracy results had a strong correlation with the Piorkowski results, however, our results differed in several noteworthy ways, potentially caused by differences in task type and code familiarity.",
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"abstract": "We present a novel graph-structured memory for visual navigation, called visual graph memory (VGM), which consists of unsupervised image representations obtained from navigation history. The proposed VGM is constructed incrementally based on the similarities among the unsupervised representations of observed images, and these representations are learned from an unlabeled image dataset. We also propose a navigation framework that can utilize the proposed VGM to tackle visual navigation problems. By incorporating a graph convolutional network and the attention mechanism, the proposed agent refers to the VGM to navigate the environment while simultaneously building the VGM. Using the VGM, the agent can embed its navigation history and other useful task-related information. We validate our approach on the visual navigation tasks using the Habitat simulator with the Gibson dataset, which provides a photo-realistic simulation environment. The extensive experimental results show that the proposed navigation agent with VGM surpasses the state-of-the-art approaches on image-goal navigation tasks. Project Page: https://sites.google.com/view/iccv2021vgm",
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"abstract": "Dynamic videos are viewed fundamentally different from static images. Besides spatial features, motion feature also plays an important role as a temporal factor. Most existing video saliency models usually employ optical flow to represent the motion feature. However, optical flow often suffers from the discontinuity problem. And we also notice that human fixations in one single video frame are much sparser than that in an identical still picture. However, many spatial saliency models take each video frame as static image independently. In this paper, we predict the dynamic visual saliency by fusing spatial and temporal features. In order to construct the temporal relationships among a set of successive frames, we introduce a smoothness operator in optical flow field to obtain more accurate motion feature. Then, considering the sparse property of video saliency, we adapt the weights of the regions surrounding to the saliency gravity center in the final maps. The experiments show that our model is more consistent with humans eye-tracking benchmarks than the state-of-the-art models.",
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"content": "Visual Question Answering (VQA) models employ attention mechanisms to discover image locations that are most relevant for answering a specific question. For this purpose, several multimodal fusion strategies have been proposed, ranging from relatively simple operations (e.g. linear sum) to more complex ones (e.g. Block [1]. The resulting multimodal representations define an intermediate feature space for capturing the interplay between visual and semantic features, that is helpful in selectively focusing on image content. In this paper, we propose a question-agnostic attention mechanism that is complementary to the existing question-dependent attention mechanisms. Our proposed model parses object instances to obtain an `object map' and applies this map on the visual features to generate Question-Agnostic Attention (QAA) features. In contrast to question-dependent attention approaches that are learned end-to-end, the proposed QAA does not involve question-specific training, and can be easily included in almost any existing VQA model as a generic light-weight pre-processing step, thereby adding minimal computation overhead for training. Further, when used in complement with the question-dependent attention, the QAA allows the model to focus on the regions containing objects that might have been overlooked by the learned attention representation. Through extensive evaluation on VQAv1, VQAv2 and TDIUC datasets, we show that incorporating complementary QAA allows state-of-the-art VQA models to perform better, and provides significant boost to simplistic VQA models, enabling them to performance on par with highly sophisticated fusion strategies.",
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"affiliation": "Australian National University and Data61-CSIRO,Canberra,Australia",
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"affiliation": "MBZUAI Abu Dhabi, UAE and Australian National University,Canberra,Australia",
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"surname": "Khan",
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{
"affiliation": "Australian National University,Canberra,Australia",
"fullName": "Nick Barnes",
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"abstract": "Recent research has shown promising results for person re-identification by focusing on several trends. One is designing efficient metric learning loss functions such as triplet loss family to learn the most discriminative representations. The other is learning local features by designing part based architectures to form an informative descriptor from semantically coherent parts. Some efforts adjust distant outliers to their most similar positions by using soft attention and learn the relationship between distant similar features. However, only a few prior efforts focus on channel-wise dependencies and learn non-local sharp similar part features directly for the degraded data in the person re-identification task. In this paper, we propose a novel Self and Channel Attention Network (SCAN) to model long-range dependencies between channels and feature maps. We add multiple classifiers to learn discriminative global features by using classification loss. Self Attention (SA) module and Channel Attention (CA) module are introduced to model non-local and channel-wise dependencies in the learned features. Spectral normalization is applied to the whole network to stabilize the training process. Experimental results on the person re-identification benchmarks show the proposed components achieve significant improvement with respect to the baseline.",
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"content": "Recent research has shown promising results for person re-identification by focusing on several trends. One is designing efficient metric learning loss functions such as triplet loss family to learn the most discriminative representations. The other is learning local features by designing part based architectures to form an informative descriptor from semantically coherent parts. Some efforts adjust distant outliers to their most similar positions by using soft attention and learn the relationship between distant similar features. However, only a few prior efforts focus on channel-wise dependencies and learn non-local sharp similar part features directly for the degraded data in the person re-identification task. In this paper, we propose a novel Self and Channel Attention Network (SCAN) to model long-range dependencies between channels and feature maps. We add multiple classifiers to learn discriminative global features by using classification loss. Self Attention (SA) module and Channel Attention (CA) module are introduced to model non-local and channel-wise dependencies in the learned features. Spectral normalization is applied to the whole network to stabilize the training process. Experimental results on the person re-identification benchmarks show the proposed components achieve significant improvement with respect to the baseline.",
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"title": "Facial Expression Recognition Based on SAS-Net Attention Mechanism",
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"abstract": "Facial expression recognition has long been a challenging task in the computer field. Most of the existing facial expression recognition methods are the extraction of facial features, but these methods emphasize that the neural network should pay attention to some key features of the expression, such as eyes, nose, and mouth. These methods ignore some minor features that also affect expression recognition, such as facial texture and other features. This paper proposes an efficient Shuffle Attention Supplement (SAS)-Net attention module to solve some minor facial feature extraction problems. SAS-Net mainly combines the feature map before entering the SAS attention module and the attention feature map formed after aggregation of the submodules in the SAS attention module. This method makes the feature map serves as an information supplement to the feature map aggregated by the sub-modules of the SAS attention module, and strengthens the connection with the minor features of the feature map instead of only focusing on the salient features. This paper will combine the ResNet network with SAS attention module and ResNet network with Shuffle Attention (SA) module to experiment on the CK+ dataset, with the accuracy of 98.9836%. The results indicate that the proposed method has the potential to improve the performance of facial expression recognition.",
"abstracts": [
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"content": "Facial expression recognition has long been a challenging task in the computer field. Most of the existing facial expression recognition methods are the extraction of facial features, but these methods emphasize that the neural network should pay attention to some key features of the expression, such as eyes, nose, and mouth. These methods ignore some minor features that also affect expression recognition, such as facial texture and other features. This paper proposes an efficient Shuffle Attention Supplement (SAS)-Net attention module to solve some minor facial feature extraction problems. SAS-Net mainly combines the feature map before entering the SAS attention module and the attention feature map formed after aggregation of the submodules in the SAS attention module. This method makes the feature map serves as an information supplement to the feature map aggregated by the sub-modules of the SAS attention module, and strengthens the connection with the minor features of the feature map instead of only focusing on the salient features. This paper will combine the ResNet network with SAS attention module and ResNet network with Shuffle Attention (SA) module to experiment on the CK+ dataset, with the accuracy of 98.9836%. The results indicate that the proposed method has the potential to improve the performance of facial expression recognition.",
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"affiliation": "School of Computer Science and Engineering Xi'an Technological University,Xi'an,Shaanxi,China,710021",
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"abstract": "Based on the collaborative virtual assembly system with haptic and force feedback, a contact elements prediction based haptic rendering method is proposed. This method is conducted at both server and client side. The server predicts the basic contact elements using haptic rendering model. At client side, according to the local proxy position and basic contact elements received from server side, the finial feedback force to be sent to PHANToM device is estimated under the invariability assumption of contact elements. This method can not only reduce the effects of both proxy position prediction error and network jitter, but also reduce the network transfer load greatly.",
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"abstract": "This paper reports our preliminary investigation of substituting haptic information in object manipulation with sound. A haptic sensation is considered to provide information such as the area of contact on the hand, the intensity of pressure, and the geometric features of the object in the contact area. In our investigation, this information was mapped to the pitch, timbre, volume level, and fluctuations in the pitch. The performance in object recognition and manipulation was evaluated, which showed that the representation of sound facilitated the performance of these tasks in non-visual conditions.",
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"abstract": "This paper reports on two experimental setups that allow the cooling of molecular ions over a wide molecular mass range. In the experiments they use /sup 138/Ba/sup +/ and /sup 9/Be as directly laser cooled ion species. /sup 9/Be/sup +/ is used as coolant for very light molecules, in particular molecular hydrogen ions. /sup 138/Ba+ can cool small di-and triatomic molecules and also offers the prospect of cooling complex, highly charged molecules with masses up to 20000 amu.",
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"abstract": "The use of ion channels as sensing elements for chemical and biological agents is a rapidly developing area. Ion channels are proteins that mediate the flow of ions and molecules across membranes such as cell walls. At Arizona State University researchers have devised a silicon ion-channel sensor. Experiments have been conducted to characterize this sensor and examine its utility in various applications. This paper presents Java functions developed to demonstrate ion channel signals and their analysis using DSP functions in class. The Java functions were developed in the J-DSP visual programming environment. Students can experiment with ion-channel signals, extract features, and differentiate signals representing the presence of different analytes.",
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"abstract": "De novo peptide sequencing is especially useful in theidentification of unknown proteins. A class of efficient modelson de novo peptide interpretation have been proposedrecently based on graph theory. However, majority of thegraph-based models convert each peak to multiple nodes inthe graph due to unknown ion types. This directly resultsin the decrease of the efficiency and accuracy of peptide sequencingalgorithms. To solve these problems, in this paperan ion transformation method is proposed to convert all theother type of ions to b-ions before the construction of spectrumgraph in the peptide sequencing. Experiments on boththe real QTof MS/MS data and the synthetic MS/MS datademonstrate that the proposed method reduces the size ofthe graph and improves the efficiency and accuracy of peptidesequencing algorithms.",
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"abstract": "The director-orientation of a liquid crystal near an interface between a glass plate and a liquid-crystal layer is studied under propagation of a shear horizontal (SH) acoustic wave. An acoustic phase-delay of a SH wave device with various liquid-crystal director orientations, which is affected by the liquid-crystal director orientation, is measured. The fractional velocity change of the SH wave is related to the liquid-crystal director orientation by numerical analysis. The dependence of the phase-delay on the applied voltage to the liquid-crystal layer is evaluated. The present technique is useful for evaluating the liquid-crystal director orientation without requiring a complicated experimental setup.",
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"abstract": "The electrorheology (ER) effect in liquids and liquid crystals are investigated by ultrasonic wave propagation. The acoustic phase-delay change, which is related to the ultrasonic wave velocity, is related to the viscosity of liquids and liquid crystals. The applied voltage dependence of acoustic phase-delay change in nematic liquid crystal, the ferroelectric liquid crystal and silicone oil are measured. The ER effect in liquid and liquid crystals are discussed in the viewpoint of liquid-crystal director orientation.",
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"affiliation": "Univ. des Sci. et Technol. de Lille, Villeneuve d'Ascq, France",
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